System and method for check fraud detection using signature validation

ABSTRACT

Systems and methods are provided for validating the authenticity of a signature on a document by providing a document from an account, the document including an actual signature and a machine-readable identifier, wherein the machine-readable identifier contains a string of data representing the integral characteristics of all valid account signatures and a person-specific confidence threshold. When the document is presented at a point of presentment, the document is scanned into a document-processing machine and the actual signature is compared against all valid account signatures.

REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationNo. 11/009,251, filed on Dec. 10, 2004, the contents of which areincorporated herein by reference.

FIELD OF THE INVENTION

This invention relates to automated document processing, and moreparticularly to automatic processing of financial documents involvingimage-based signature verification.

BACKGROUND OF THE INVENTION

In general, financial institutions have automated most check processingsystems by printing financial documents, such as account numbers andbank routing numbers onto the checks. Before a check amount is deductedfrom a payer's account, the amount, account number, and other importantinformation must be extracted from the check. The highly automated formof extraction is done by a check processing control system that capturesinformation from the Magnetic Ink Character Recognition (“MICR”) line.The MICR line consists of specially designed numerals that are printedon the bottom of a check using magnetic ink. The MICR data fieldsinclude the bank routing number, bank transit number, account number,check serial number, check amount, process code and extended processcode.

Check fraud is one of the largest challenges facing financialinstitutions today. Advances in counterfeiting technology have made itincreasingly easy to create realistic counterfeit checks used to defraudbanks and other businesses. Image-based check processing systems play acrucial role in check fraud detection software programs by extractingand verifying various check features that can be found on the checkimage. In order to be verifiable, an image feature should be eitherconsistent across all check images from the same account, orcross-verifiable against another feature on the same check.

Conventional methods of reducing check fraud include providingwatermarks on the checks, fingerprinting non-customers that seek to cashchecks, positive pay systems and reverse positive pay systems. Positivepay systems feature methods in which the bank and its customers worktogether to detect check fraud by identifying items presented forpayment that the customers did not issue. With reverse positive paysystems, each bank customer maintains a list of checks issued andinforms the bank which checks match its internal information. Althoughthese check fraud security systems have been somewhat effective indeterring check fraud, they suffer from a multiplicity off drawbacks.For example, these systems are generally very slow and prohibitivelyexpensive.

U.S. Pat. No. 5,257,320 discloses a signature verification systemwherein, a check is scanned for an actual signature and a correspondingcode located on the face of the check. The scanned data is convertedinto digital form and a software program is used to compare thesignature to the code. A pass-fail light is then employed to indicatethe result of the comparison. U.S. Pat. No. 5,509,692 teaches a systemand method for point of presentation signature verification for amonetary instrument such as a check, wherein the front face of the checkcomprises a machine-readable representation of an authorized signature.At a point of presentment, the check is scanned and the actual signatureon the check is manually or automatically compared with themachine-readable representation of the authorized signature, and asimilarity score is generated

One drawback of the above-identified signature verification systems isthat they do not involve a compression of account signature data to afingerprint containing only a small fraction of the account signaturedata. These references also fail to disclose methods of determiningperson-specific confidence thresholds by evaluating the complexity andtopology of account signatures. In addition, these references providepreprocessing of the signature and extraction features from thesignature bitmap rather than applying a signature skeletonizationtechnique, and then extracting features from the signature skeleton. Afurther drawback of the above-identified systems is that they assume afixed-size signature representation.

In view of the above drawbacks, there exists a need for a system andmethod for check fraud detection using signature validation thatinvolves a compression of account signature data to a fingerprintcontaining only a small fraction of the account signature data.

There further exists a need for a system and method for check frauddetection using signature validation that involves determiningperson-specific confidence thresholds by evaluating the complexity andtopology of account signatures.

It would also be desirable to provide a system and method for checkfraud detection using signature validation that involves applying asignature skeletonization technique, and then extracting features fromthe signature skeleton.

It would further be desirable to provide a need for a system and methodfor check fraud detection using signature validation does not assume anyparticular size of the signature.

SUMMARY OF THE INVENTION

In view of the forgoing, it is an object of the present invention toprovide a system and method for check fraud detection using signaturevalidation that involves a compression of account signature data to afingerprint containing only a small fraction of the account signaturedata.

It is a further object of the invention to provide a system and methodfor check fraud detection using signature validation that involvesdetermining person-specific confidence thresholds by evaluating thecomplexity and topology of account signatures.

It is an additional object of the invention to provide a system andmethod for check fraud detection using signature validation thatinvolves applying a signature skeletonization technique, and thenextracting features from the signature skeleton.

It is yet another object of the invention to provide a system and methodfor check fraud detection using signature validation does not assume anyparticular size of the signature.

The present invention provides a system and method of image-based frauddetection for checking the authenticity of a signature on a financialdocument such as a check. The system and method preferably areimplemented using computer software programs comprising machine readableinstructions for detecting fraudulent checks and verifyingnon-fraudulent checks. Advantageously, the system and method of thepresent invention may be employed to validate signatures and detectcheck fraud at various points of presentment, for example at tellerstations or at retail stores.

One aspect of the present invention features signature encoding softwarefor creating a unique “fingerprint,” encoding the fingerprint into abarcode and printing the barcode onto a series of checks. Thefingerprint preferably comprises a string of data representing theintegral characteristics of all valid account signatures and aperson-specific confidence threshold. According to some embodiments, thefingerprint comprises a machine-readable data string of about 100 bytesof data or less. A fingerprint is created using a signature encodingalgorithm that converts the signature image into the machine-readabledata string. Additional fingerprints may be included in the data stringcorresponding to further authorized individuals. In this manner, thedata string is the master data source against which all signatures fromthis account will be compared.

Another aspect of the present invention involves validating theauthenticity of a signature on a document by creating a fingerprintcomprising machine-readable data corresponding to authorized accountsignatures, the fingerprint comprising a skeletonized version of theaccount signature data. Further steps involve providing a document froman account, the document including an actual signature and afingerprint, presenting the document at a point of presentment, scanningthe document, including the actual signature and the fingerprint, into adocument-processing machine and comparing the actual signature againstthe account signature data in the fingerprint.

A further aspect of the present invention involves encoding a documentwith account signature data for signature validation comprisingcompressing the account signature data into a fingerprint comprisingmachine-readable data corresponding to one or more account signaturessuch that the fingerprint contains only a small fraction of the accountsignature data, encoding the fingerprint into a barcode and printing thebarcode on the document. The fingerprint represents integralcharacteristics of all valid account signatures and may identify aperson-specific confidence threshold for each account user. According tosome embodiments, the fingerprint comprises 100 bytes of data or less.

An additional aspect of the present invention involves encoding adocument with account signature data for signature validation comprisingcompressing the account signature data to a fingerprint comprisingmachine-readable data corresponding to one or more account signatures,evaluating the complexity and topology of each account signature,encoding the fingerprint into a barcode and printing the barcode on thedocument. This aspect may further involve determining a person-specificconfidence threshold for each account signature based upon thecomplexity and topology of each account signature.

Yet another aspect of the present invention involves locating one ormore signatures on a check comprising receiving an original orpreprocessed check bitmap, dividing the check bitmap into fixed-sizetiles, sorting the tiles into predefined classes, identifyinghandwriting tiles, adjusting the signature positions using a connectedcomponents analysis and generating final signature locations. Sortingthe tiles into predefined classes may include applying neural networkclassifiers to the tiles.

A further aspect of the present invention involves locating one or moresignatures on a check, comprising receiving an original or preprocessedcheck bitmap having a predetermined signature default area, determiningthe location of one or more check layout elements, using the location ofthe one or more check layout elements to determine potential signaturelocations and employing a handwriting detection technique to detect thepresent or absence of handwriting in the potential signature locations.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages of the invention will becomemore apparent upon reading the following detailed description and uponreference to the accompanying drawings.

FIG. 1 illustrates a financial document having a machine-readablebarcode;

FIG. 2 is a flowchart for a method of determining the location of one ormore signature on a document according to the principles of the presentinvention;

FIG. 3 is a flowchart depicting the computation of initial uncompressedfeatures used for signature validation in accordance with the principlesof the present invention; and

FIG. 4 is a flowchart depicting the computation of compressed featuresused for signature validation in accordance with the principles of thepresent invention.

DETAILED DESCRIPTION

In the following paragraphs, the present invention will be described indetail by way of example with reference to the attached drawings.Throughout this description, the preferred embodiment and examples shownshould be considered as exemplars, rather than as limitations on thepresent invention. As used herein, the “present invention” refers to anyone of the embodiments of the invention described herein, and anyequivalents. Furthermore, reference to various feature(s) of the“present invention” throughout this document does not mean that allclaimed embodiments or methods must include the referenced feature(s).

The present invention provides a system and method of image-based frauddetection for checking the authenticity of a signature on a financialdocument such as a check by signature validation. The system and methodpreferably are implemented using computer software programs comprisingmachine readable instructions for creating a data string correspondingto an authorized account user and checking subsequent signatures againstthe data string. According to some embodiments, the computer software isadapted to be installed on a personal computer running a MicrosoftWindows or other operating system.

Referring to FIG. 1, financial documents such as checks commonly undergoautomated processing. Check 100 includes amount field 105, signaturefield 110, and a barcode 115. When the check is received by a financialinstitution, it is processed to ensure the proper amount of money isdebited from the proper account. The location of the fields in the check100 is determined by banking regulations. These regulations ensure, forexample, that the amount field 105 and the barcode 115 are in the samelocation for each check 100 regardless of which institution issued thecheck 100. By regulating the location of this information, automatedsystems can be used to process the checks 100.

According to an aspect of the present invention, signature encodingsoftware is employed (e.g., by a bank) to: (1) analyze the signatures onan account; (2) create a unique “fingerprint” for one or more signatureson the account; and (3) encode the fingerprint into a barcode such asbarcode 115. After encoding the fingerprints, the barcodes preferablyare sent to a printer for check stock printing with embedded barcodeinformation created by the signature encoding software. The fingerprintpreferably comprises data corresponding to one or more unique accountsignatures corresponding to each authorized account user.

According to a preferred embodiment, the fingerprint only includes datacorresponding to a small fraction of the actual account signatures. Moreparticularly, the fingerprint comprises a machine-readable data string,preferably about 500 bytes or less, most preferably about 100 bytes ofdata or less. By contrast, if data matching the entire accountsignatures were to be encoded, the resulting fingerprint would comprisethousands (or perhaps millions) of bytes of data. According to someembodiments, fingerprints are created using a signature encodingalgorithm that converts signature images into a machine-readable datastring. For an added degree of security, the signature encodingalgorithm may be different for different accounts.

According to another aspect of the present invention, signaturevalidation software is embedded into one or more devices at variouspoints of presentment. Points of presentment include, but are in no waylimited to, bank teller stations, retail checkout lines, and grocerystore checkout lines. One suitable point of presentment device is apoint of sale (POS) scanner, which preferably is adapted to: (1) scan acheck and read the barcode; (2) extract a signature-related portion ofthe barcode data; (3) validate the actual signature against informationstored in the barcode; and (4) display an alert if the signature isdeemed illegitimate. Advantageously, such POS scanners do not require anetwork connection to determine the authenticity of a check.

When banking customers order check stock (e.g., at a bank), thesignature encoding software of the present invention is used to analyzethe signatures on the account and create a unique barcode. In apreferred embodiment, the barcodes are two-dimensional barcodes adaptedto meet the PDF417 standard, which is a multi-row, variable lengthbarcode symbol having high capacity and error correction. This standardis capable of encoding more than 1,100 bytes, 1,800 ASCII characters or2,700 digits depending on the data compaction mode. Additionally, PDF417supports full ASCII character including extended character set.

There currently exist three configurations of PDF417, namely: (1)Standard (which is the base of all PDF417 extended versions); (2) Macroand (3) Truncated. In operation, the unique barcode is sent (along withthe customer and check stock information) to the printer for printing.The printer then prints the check stock with the embedded barcode anddistributes the checks to the appropriate bank customers. When one ofthe checks is subsequently presented, it is scanned by a POS scanner,which analyzes the signature and compares it with the information storedin the barcode. If the signature does not match the information, thescanner alerts the user that the check could be fraudulent.

According to a preferred embodiment, one or more actual signatures of anauthorized individual are used to create a single 100-byte string ofdata for a given account. This data string comprises a fingerprint thatis the master data source against which all signatures from the accountwill be compared. The account's unique barcode preferably includes thedata string for signature validation. One or more images of a validaccount signatures are scanned into the system using a scanner and acorresponding 100-byte fingerprint is computed.

When a check is presented, for example at a retail store, it is scannedinto a POS scanner having the signature validation software of thepresent invention installed thereon. At this point, the signaturevalidation software automatically locates the actual signature on thecheck and makes a comparison between the actual signature and the validaccount signature(s) (i.e., the 100-byte data string located on thebarcode for that account). The software preferably is adapted to outputa confidence value indicating whether the actual signature is anauthentic signature. The signature validation software preferablyautomatically locates the actual signature on a check when the check isscanned into a POS scanner.

According to a preferred implementation of the invention, the signaturevalidation software utilizes a plurality of techniques to moreaccurately and precisely locate the signatures on various checks. Suchtechniques include, but are not limited to: (1) the use of a predefineddefault area on the check; (2) the use of an adjusted predefined defaultarea on the check; and (3) the use of a handwriting detection technique.The use of a predefined default area on the check is the most basic ofthese signature location techniques. This approach is suitable forpersonal checks since most personal checks contain a signature area in aknown location. However, personal checks having abnormalities (e.g., thepresence of a second MICR line) may occasionally produce spuriousresults on personal checks. The use of a predefined default area is notparticularly suitable for business checks, which tend to varydramatically in size and signature location.

The use of an adjusted predefined default area on the check is a morerobust approach that permits an adjustment of the default area basedupon an analysis of the location of one or more check layout elements,such as MICR lines, signature underlines and micro-print inscriptions.The signature validation software of the present invention isadvantageously adapted to precisely locate MICR lines on both personaland business checks. In addition, the software is adapted to preciselylocate signature underlines and micro-print inscriptions on bothpersonal and business checks. The precise locations of these checklayout elements are indicative of the location of the actualsignature(s) on the check.

The signature validation software of the present invention also featuresa handwriting detection technique adapted to locate any handwriting thatis present on the front face of the check. This handwriting detectiontechnique provides the most accurate and precise location of actualsignature(s) on both personal and business checks. According to apreferred embodiment, the software of the present invention uses acombination of the signature location techniques. By way of example,most business checks contain more than one signature position. Theadjusted default area technique may be used to detect the potentialsignature positions (e.g., the identification of two signatureunderlines indicates the presence of two potential signature areas).After the detection of potential signature areas, the handwritingdetection technique of the present invention may be used to detect thepresent or absence of handwriting in the potential signature areas.

According to a preferred embodiment of the invention, the handwritingdetection technique generates final signature locations according to thefollowing method. Referring to FIG. 2, step 120 involves receiving theoriginal or preprocessed check bitmap. The next step (step 130) involvesdividing the bitmap into fixed-size tiles (e.g., 32×32 or 64×64). Step135 involves applying neural network classifiers to sort tiles into thepredefined classes. Such classes may include printed text, cursive text,handwritten text, graphics, picture and icon. The next step (step 140)involves calculating a confidence score for each classification (e.g., aconfidence score of 0.99 may indicate a 99 percent chance theclassification being correct).

After confidence scores have been calculated, the next steps involveidentifying handwriting tiles (e.g., tiles that contain printed text,cursive text and/or handwritten text) (step 145) and grouping thehandwriting tiles using a cluster analysis technique (step 150).According to the cluster analysis technique, clusters of handwritingrepresent possible signature locations. The method may also involvereviewing other tiles that have been classified as graphics andselectively adding some of these tiles to the signature clusters. Thenext steps involves adjusting the signature positions using connectedcomponents analysis (step 155) and generating final signature locations(step 160).

In accordance with the principles of the present invention, thefollowing features of the signature validation software will now bedescribed: (1) initial (uncompressed) features used for signaturevalidation; (2) compressed features (i.e., the signature fingerprint)used for signature validation; (3) a method for comparing the featurevectors of a signature; (4) a method for topological analysis of thesignature; (5) a method for evaluating the complexity of signature; (7)a method for evaluating the variability of the same person's signatures;and (8) a method for computing a person-specific confidence threshold.

Referring to FIG. 3, a flowchart depicting the computation of initialuncompressed features used for signature validation is provided. In theillustrated embodiment, the initial features comprise slant, adjacencystatistics, Hough Transform, intersections statistics and densitystatistics. Referring to step 200, a bitmap of one or more accountsignatures is initially provided. Once the signature is detected, itsimage is cropped from the check image to produce a signature snippet. Instep 210, a thinning algorithm is employed to build a one-dimensional“skeleton” of the signature.

The thinning algorithm used for skeletonization produces aone-dimensional graph that is particularly suitable for featureextraction. Instead of merely analyzing the raw signature bitmap, thethinning algorithm is first applied to create the signature skeleton,and then selected features are extracted from the skeleton. Oneadvantage of this approach is that it combines both raster and vector(graphic) signature description. The skeleton may be furtherpreprocessed (e.g., smoothed) to make the graph less dependent on therandom variations of the signature. After building the one-dimensionalskeleton, the signature is analyzed for slant, adjacency statistics,Hough Transform, intersections statistics and density statistics.

With respect to slant, step 320 involves building a projection histogram(array of integer values Hist) for each angle within a reasonable rangewith 0.1-degree increment. Given the projection angle A, each blackpixel P on the signature bitmap increments Hist [Q] by 1, where Q is theposition onto which the pixel P has been projected. Given the projectionangle A, the next step involves counting the variance Var (the square ofthe standard deviation), wherein:Sum=Σ Hist[Q](over all Q values);   (1)SumSq=Σ (Hist[Q]*Hist[Q])(over all Q values); and   (2)Var=(SumSq−((Sum*Sum)/N))/N, where N is the number of Q-values   (3)Step 330 involves locating and selecting the angle for which thevariance assumes a maximal value. Step 340 involves normalizing thegenerated arrays to be size- and resolution-independent. As depicted inbox 350, the total number of slant features is 1.

With respect to adjacency statistics, an initial step (step 360)comprises computing a “neighborhood byte” for every black point,wherein:

(1) Initial value of the byte is 0;

(2) Set bit #0 in the byte to 1 if the pixel has neighbor in the leftdirection; and

(3) Set bit #1 in the byte to 1 if the pixel has neighbor in the rightdirection and so on

using the “pre-assigned” bit for all 8 directions (including diagonals).According to some embodiments, the step of computing the neighborhoodbyte includes the step of analyzing 3×3 neighbors for each black point.The resulting neighborhood byte is a value from 0 to 255. The next stepsinvolve dividing the image into 4 parts (step 370) and counting thenumber of times that each possible neighborhood byte value (from 0 to255) has occurred in each part (step 380). Step 390 involves normalizingthe generated arrays to be size- and resolution-independent. Referringto box 400, the distribution for each part is represented by a256-element array of integer value (that latter occupies 4 bytes inmemory), such that the total number of features in the adjacencystatistics is 1024 (256*4).

With respect to building a Hough Transform group, an initial step (step410) comprises dividing the signature bitmap in the horizontal directioninto 24 parts. Step 420 involves obtaining projections of the image onthe 24 parts, for example using 41 angle values from −45° to 45°. Step430 involves normalizing the generated arrays to be size- andresolution-independent. Referring to box 440, since each projection isrepresented by a 24-component vector, the total number of features inthe Hough Transform group is 984 (41*24). With respect to buildingintersections statistics, an initial step (step 450) involves countingthe number of intersections (with black pixels) for each scan line ofthe signature bitmap.

With further reference to FIG. 3, step 460 involves counting the numberof scan lines having exactly 0 intersections, 1 intersection, 2intersections, 3 intersections, etc. According to some embodiments, themaximum number of intersections is selected to be 127 such that theintersections statistics are represented by 128 features (box 480). Step470 involves normalizing the generated arrays to be size- andresolution-independent. With respect to building density statistics, aninitial step (step 490) comprises dividing the image into 10 partshorizontally and 4 on parts vertically, thereby producing a total of 40tiles or features. Step 500 involves counting the number of black pixelsin each tile. Step 510 involves normalizing the generated arrays to besize- and resolution-independent. As depicted in box 520, the totalnumber of density statistics features is 40. Referring to box 530, thetotal number of initial features is 2177 with 4 bytes per feature.

According to another aspect of the present invention, the initialfeatures are compressed by selecting only the most informative features,thereby preferably reducing the fingerprint to about 500 bytes or less,most preferably about 100 bytes of data or less. To achieve the desireddata reduction, a signature verification testing environment including atest deck and testing utilities was built. Various testing wasperformed, including: (1) experimenting with the number of parts that asignature should be segmented into; (2) applying various statisticalanalyses to the test results, given that the final set of featuresshould contain 100 bytes or less; (3) selecting the most useful andinformative features; and (4) optimizing the feature weights within anoverall confidence score produced by the system.

Referring to FIG. 4, a flowchart depicting the computation of compressedfeatures (i.e., slant, adjacency statistics, Hough Transform,intersections statistics and density statistics) used for signaturevalidation is provided. Referring to box 530, the total number ofinitial features is 2177 with 4 bytes per feature, wherein: (1) thetotal number of slant features is 1 (box 350); (2) the total number ofadjacency statistics features is 1024 (box 400); (3) the total number ofHough Transform features is 984 (box 440); (4) the total number ofintersections statistics features is 128 (box 480); and (5) the totalnumber of density statistics features is 40 (box 520). Regarding slantfeatures, in step 540 the generated arrays are again normalized to besize- and resolution-independent. Referring to box 550, the total numberof compressed slant features remains 1.

With respect to adjacency statistics features, step 560 comprisesreducing the number of adjacency statistics by only accounting for blackpixels having 1 or 2 neighbors (i.e., using only neighborhood bytes with1 or 2 non-zero bits). This compresses or reduces the number ofadjacency statistics features from 1024 to 36. In step 570, thegenerated arrays are normalized to be size- and resolution-independent.Referring to box 580, the total number of compressed adjacencystatistics features is 36 and a running total of compressed features isfrom 2 to 37. Regarding Hough Transform features, step 590 involvesreducing the number of parts to 6 and reducing the number of angles to5, thereby compressing the number of features from 984 to 30. In step600, the generated arrays are normalized to be size- andresolution-independent. Referring to box 610, the total number ofcompressed adjacency statistics features is 30 and a running total ofcompressed features is from 38 to 67.

With respect to intersections statistics features, step 620 comprisesreducing the number of intersections statistics by summing theintersections from 12 to 127 and considering the sum as a singlefeature. This compresses the number of intersections statistics featuresfrom 128 to 12. In step 630, the generated arrays are normalized to besize- and resolution-independent. Referring to box 640, the total numberof compressed intersections statistics features is 12 and a runningtotal of compressed features is from 68 to 79. Regarding densitystatistics features, step 650 involves dividing the image on a smallernumber of parts (18=6×3), thereby compressing the number of featuresfrom 40 to 18. In step 660, the generated arrays are normalized to besize- and resolution-independent. Referring to box 670, the total numberof density statistics features is 18 and the total number of compressedfeatures is from 80 to 97. Referring to box 680, the total number ofcompressed features is 97 with 1 byte per feature.

According to the principles of the present invention, the featurevectors may be analyzed and compared using the following formulae,wherein weighted distances are used to compare the feature vectors.Given two feature vectors F={f1, . . . ,f97} and G={g1, . . . ,g97}, thedistance D=D(F, G) between the vectors is defined as:$D = {{w\quad 1*{{abs}\left( {{g\quad 1} - {f\quad 1}} \right)}} + {w\quad 2*{\sum\limits_{i = 2}^{9}{{abs}\left( {{gi} - {fi}} \right)}}} + {w\quad 3*{\sum\limits_{i = 10}^{37}{{abs}\left( {{gi} - {fi}} \right)}}} + {w\quad 4*{\sum\limits_{i = 38}^{67}{{abs}\left( {{gi} - {fi}} \right)}}} + {w\quad 5*{\sum\limits_{i = 68}^{79}{{abs}\left( {{gi} - {fi}} \right)}}} + {w\quad 6*{\sum\limits_{i = 80}^{97}{{{abs}\left( {{gi} - {fi}} \right)}.}}}}$According to some embodiments, the following weighting coefficients areemployed: w1=2, w2=10, w3=20, w4=2, w5=10, w6=20. The confidence(likelihood) of F and G representing the signatures of the same personis defined as C=max(0, 100−(D/128)). If confidence C is greater than apredetermined threshold, the two signatures are deemed to belong to thesame person. Otherwise, the signatures are deemed to belong to differentpersons. Methods for computing person-specific confidence thresholds aredescribed herein.

A further aspect of the present invention involves a method of analyzingof the topological features of a signature to assist in evaluating thecomplexity of a signature. Such a method may comprise one or more of thefollowing steps: (1) computing the number of connected components in thesignature; (2) computing the number of holes in the components; (3)computing the sizes of the components and sizes of the holes; (4) usinga thinning algorithm to build a 1-dimensional “skeleton” of thesignature (note that most black pixels have two black neighbors afterthinning, whereas some pixels have only one neighbor (“tails”), whileother pixels have 3 or 4 neighbors (“branches”)—such points are“critical” and the number of neighbors is the topological index of thecritical point; (5) building a list of all critical points, preferablyincluding positions and topological indices; (6) building a descriptionof every component as a list of critical points; and (7) for each pairof adjacent critical points, adding a description of the “path” thatconnects the points inside the skeleton (note that the path parametersare length, instant directions at the beginning and the end of the path,global direction and curvature).

In accordance with an additional aspect of the present invention, thetopological features of a signature are used in evaluating thecomplexity of the signature for computing person-specific confidencethresholds. Overall complexity of a particular signature is a functionof the topological features, which may be analyzed as described in thepreceding paragraph. The following topological features (F1-F7) have thegreatest impact on the complexity: (1) the number of connectedcomponents (F1, determined by computing the number of connectedcomponents in the signature); (2) the number of holes in connectedcomponents (F2, determined by computing the number of holes in thecomponents); (3) the size of connected components (F3, determined bycomputing the sizes of the components and sizes of the holes); (4) thenumber of critical points with a topological index of 1 (F4, determinedby building a list of all critical points including positions andtopological indices and building a description of every component as alist of critical points); (5) the number of critical points with atopological index of 3 (F5, see F4); (6) the number of critical pointswith a topological index of 4 (F6, see F4 and F5); and (6) the averagecurvature between adjacent critical points (F7, determined by adding adescription of the path that connects the points inside the skeleton foreach pair of adjacent critical points). According to a preferredembodiment of the present invention, a weighted sum of features is usedto represent the signature complexity, wherein: C=Σ W_(i)*F_(i)(I=1-7),where Fi are features F1 to F7.

In accordance with another aspect of the present invention, a method ofassessing signature variability for computing person-specific confidencethresholds is provided. Of course, more than one actual signature mustbe available in order to measure signature variability. According to apreferred embodiment, at least 5 actual signatures are used to assesssignature variability. An initial step comprises building a featurevector for each signature for each account user, for example asdescribed hereinabove with respect to FIGS. 2 and 3. In this manner,feature vectors F₁, F₂, . . . F_(N), are calculated for each accountuser, wherein N is the number of signatures. The next step involvescomputing confidences C(i,j)=C(Fi, Fj) for each pair of differentsignatures {I<J), as described hereinabove. The next step involvescomputing the standard deviation within the following set ofconfidences:Sum=Σ C(I,J) (over all I<J);   (1)SumSq=Σ(C(I,J)*C(I,J)) (over all I<J);   (2)Var=(SumSq−((Sum*Sum)/P))/P, where P is the number of pairs withI<J:P=N*(N−1)/2, where N is the number of signatures; and   (3)V=sqrt (Var), where V is the standard deviation used to represent thevariability in question.   (4)

Once the signature variability and average signature complexity havebeen determined, one can compute a person-specific confidence threshold,wherein:

(1) Threshold=AveThr−W1*ComplexityDelta−W2*VariabilityDelta;

(2) where AveThr is the average threshold value, experimentallyestablished as 67 (on a 0-100 scale);

(3) where ComplexityDelta is computed as (C−AveComplexity), whereAveComplexity is average complexity of signatures (establishedexperimentally);

(4) where VariabilityDelta is computed as (V−AveVariability), whereAveVariability is average variability of the same person's signatures(established experimentally); and

(5) where the weights W1 and W2 are some positive values that areestablished experimentally according to the trade-off between falsepositive and false negative rates.

The above-described person-specific confidence threshold formula willnow be considered in view of the following cases. In the case where bothdeltas are approximately 0, the particular account user has asubstantially standard signing style, wherein Threshold is approximatelyequal to AveThr. In the case where ComplexityDelta is approximately 0,but the VariabilityDelta is substantially greater than 0, the accountuser has no established signing style such that the signatures do notclosely match each other. Since W2 is a positive value preceded by aminus sign, the Threshold will become less than AveThr. Thus, to avoidtoo many false rejections of this user's signature (due to the highvariability), the person-specific confidence threshold should be loweredin this instance. In the case where ComplexityDelta is approximately 0,but the VariabilityDelta is much smaller than 0, the account user has awell established signing style and the signatures very closely matcheach other). Since W2 is a positive value preceded by minus sign, theThreshold will become greater than AveThr. Thus, the person-specificconfidence threshold may be increased to reduce the number of falseacceptances (false positive decisions), yet not significantly increasethe occurrence of false negative decisions.

In the case where VariabilityDelta is 0, but ComplexityDelta is muchgreater than 0, the account user has a very complex signature. Since W1is a positive value preceded by minus sign, the Threshold will becomesmaller than AveThr. In this example, to avoid too many false rejections(false negative decisions) of this user's signature because of the highcomplexity, the person-specific confidence threshold should be lowered.However, the chance of a false positive decision doesn't increase muchsince the signature is complex and therefore difficult to forge. In thecase where VariabilityDelta is 0, but the ComplexityDelta is muchsmaller than 0, the person has very simple signature. Since W1 is apositive value preceded by minus sign, the Threshold will become greaterthan AveThr. In this example, the person-specific confidence thresholdshould be increased to reduce the number of false positive decisionssince the signature is simple and therefore easy-to-forge. Increase thethreshold does not significantly increase the chance of false negativedecisions since the signature is simple and the authentic account userwill likely be able to produce a consistent signature.

Thus, it is seen that a system and method for check fraud detection andprevention is provided. One skilled in the art will appreciate that thepresent invention can be practiced by other than the various embodimentsand preferred embodiments, which are presented in this description forpurposes of illustration and not of limitation, and the presentinvention is limited only by the claims that follow. It is noted thatequivalents for the particular embodiments discussed in this descriptionmay practice the invention as well.

1-10. (canceled)
 11. A method of locating one or more signatures on acheck, comprising the steps of: receiving an original or preprocessedcheck bitmap; dividing the check bitmap into fixed-size tiles; sortingthe tiles into predefined classes; identifying handwriting tiles;adjusting the signature positions using a connected components analysis;and generating final signature locations.
 12. The method of claim 11,wherein the step of sorting the tiles into predefined classes includesthe step of applying neural network classifiers to the tiles.
 13. Themethod of claim 11, wherein the predefined classes include printed text,cursive text, handwritten text, graphics, picture and icon.
 14. Themethod of claim 11, further comprising the step of calculating aconfidence score corresponding to the classification of each tile. 15.The method of claim 11, further comprising the step of grouping theidentified handwriting tiles using a cluster analysis technique, whereinclusters of handwriting represent potential signature locations.
 16. Amethod of locating one or more signatures on a check, comprising thesteps of: receiving an original or preprocessed check bitmap having apredetermined signature default area; determining the location of one ormore check layout elements; using the location of the one or more checklayout elements to determine potential signature locations; andemploying a handwriting detection technique to detect the present orabsence of handwriting in the potential signature locations.
 17. Themethod of claim 16, wherein the check layout elements comprise MICRlines, signature underlines and micro-print inscriptions.
 18. The methodof claim 16, wherein the step of employing a handwriting detectiontechnique comprises the steps of dividing the check bitmap intofixed-size tiles and sorting the tiles into predefined classes;
 19. Themethod of claim 18, further comprising the step of identifyinghandwriting tiles.
 20. The method of claim 19, further comprising thestep of adjusting the signature positions using a connected componentsanalysis.